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This research identifies the annotation boundary between loanwords and code-switching as the primary bottleneck in processing Kazakh-Russian bilingual text, rather than limitations of specific model architectures like XLM-R or FastText. By releasing a gold-standard dataset that distinguishes integrated borrowings from clause-level switches, it provides essential infrastructure for improving low-resource NLP tasks involving shared Cyrillic scripts. The study demonstrates that refining data guidelines and filtering cascades yields more significant performance gains than simply upgrading the underlying language model class.
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Loanword or Switch? The Annotation Boundary, Not the Model, Drives Kazakh-Russian Code-Switching Identification

arXiv2 min read

arXiv:2608.00581v1 Announce Type: cross Abstract: Off-the-shelf LID and letter heuristics over-label Kazakh-Russian social text as mixed: Russian loanwords inside Kazakh look like code-switching under a shared Cyrillic script. We release a document-level gold LID set whose guideline keeps integrated borrowings as Kazakh and reserves mixed for clause-level switches, plus a mixed-only sentiment pool used after LID in a filter-first cascade. On a shared LID test, FastText, Lingua, raw and windowed HeLI, character-trigram NB, and XLM-R range from weak to strong performance. The gap shows the bottleneck is the loanword-vs-switch annotation boundary, not model class alone.

Title:Loanword or Switch? The Annotation Boundary, Not the Model, Drives Kazakh-Russian Code-Switching Identification

Abstract:Off-the-shelf LID and letter heuristics over-label Kazakh-Russian social text as mixed: Russian loanwords inside Kazakh look like code-switching under a shared Cyrillic script. We release a document-level gold LID set whose guideline keeps integrated borrowings as Kazakh and reserves mixed for clause-level switches, plus a mixed-only sentiment pool used after LID in a filter-first cascade. On a shared LID test, FastText, Lingua, raw and windowed HeLI, character-trigram NB, and XLM-R range from weak to strong performance. The gap shows the bottleneck is the loanword-vs-switch annotation boundary, not model class alone.

5 pages. Preprint. Submitted to W-NUT 2026. Code and data: this https URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.00581 [cs.CL]

(or arXiv:2608.00581v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.00581 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 214 words

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